Visualizing Communities in Dynamic Networks

MARIANO G. BEIRO; JORGE R. BUSCH; J IGNACIO ALVAREZ-HAMELIN

Buenos Aires

Workshop; Latin-American Workshop on Dynamic Networks; 2010

Community structure is relevant to understand theproperties of social networks and predict their behavior. Butwhen this study includes the dynamic evolution, finding thesecommunities and following them through time can be evenmore useful: it may help us to understand how social networksgrow and to develop constructive models. In this article weanalyze a dynamic blog dataset with a static community detectionalgorithm based on modularity, and then we use a similaritymeasure in order to follow the communities through time. Finallywe develop a tool to visualize the dynamics of the network. Thistool provides a fast intuition about the evolution of the communitystructure.